def __init__(self):
AmiciExample.__init__(self)
self.numX = 1
self.numP = 5
self.numK = 0
self.numZ = 0
self.modelOptions['theta'] = np.log10([0.1, 1000, 2, 8e-1, 1.6])
self.modelOptions['ts'] = np.linspace(0, 20, 100)
self.modelOptions['pscale'] = 2
#self.modelOptions['qpositivex'] = [0] * self.numX
self.solverOptions['atol'] = 1e-12
self.solverOptions['maxsteps'] = 1e4
self.solverOptions['nmaxevent'] = 2
self.solverOptions['rtol'] = 1e-14
self.solverOptions['sens_ind'] = []
self.solverOptions['sensi'] = 0
self.solverOptions['sensi_meth'] = 1
self.data['Y'] = np.full((len(self.modelOptions['ts']), 1), np.nan)
self.data['Sigma_Y'] = np.full((len(self.modelOptions['ts']), 1),
np.nan)
self.data['Z'] = np.full((self.numZ, self.solverOptions['nmaxevent']),
np.nan)
self.data['Sigma_Z'] = np.full(
(self.numZ, self.solverOptions['nmaxevent']), 0.5)
self.data['condition'] = self.modelOptions['kappa']
self.data['t'] = self.modelOptions['ts']
def __init__(self):
AmiciExample.__init__(self)
self.numZ = 2
self.numX = 3
self.numP = 4
self.numK = 4
self.modelOptions['theta'] = np.log10([0.5, 2, 0.5, 0.5])
self.modelOptions['kappa'] = [4.0, 8.0, 10.0, 4.0]
self.modelOptions['ts'] = np.linspace(0.0, 10.0, 20)
self.modelOptions['pscale'] = 2
self.modelOptions['qpositivex'] = [0] * self.numX
self.solverOptions['atol'] = 1e-16
self.solverOptions['maxsteps'] = 1e4
self.solverOptions['nmaxevent'] = 2
self.solverOptions['rtol'] = 1e-8
self.solverOptions['sens_ind'] = []
self.solverOptions['sensi'] = 0
self.solverOptions['sensi_meth'] = 1
self.data['Y'] = np.full((len(self.modelOptions['ts']), 1), np.nan)
self.data['Sigma_Y'] = np.full((len(self.modelOptions['ts']), 1),
np.nan)
self.data['Z'] = np.full((self.solverOptions['nmaxevent'], self.numZ),
np.nan)
self.data['Sigma_Z'] = np.full(
(self.solverOptions['nmaxevent'], self.numZ), np.nan)
self.data['condition'] = self.modelOptions['kappa']
self.data['t'] = self.modelOptions['ts']
def __init__(self):
AmiciExample.__init__(self)
self.numX = 9
self.numP = 17
self.numK = 2
curPath = os.path.dirname(os.path.realpath(__file__))
dataPath = curPath + "/../../matlab/examples/example_jakstat_adjoint/pnas_data_original.xls"
xls = pd.ExcelFile(dataPath).parse()
self.modelOptions['ts'] = xls.time
self.modelOptions['theta'] = np.array([
0.60, 3, -0.95, -0.0075, 0, -2.8, -0.26, -0.075, -0.41, -5, -0.74,
-0.64, -0.11, 0.027, -0.5, 0, -0.5
])
self.modelOptions['kappa'] = [1.4, 0.45]
self.modelOptions['pscale'] = 2
#self.modelOptions['qpositivex'] = [0] * self.numX
self.solverOptions['atol'] = 1e-12
self.solverOptions['maxsteps'] = 1e4
self.solverOptions['nmaxevent'] = 10
self.solverOptions['rtol'] = 1e-12
self.solverOptions['sensi'] = 0
self.solverOptions['sensi_meth'] = 1
self.data['Y'] = np.array(
xls.loc[:, ['pSTAT_au', 'tSTAT_au', 'pEpoR_au']])
self.data['Sigma_Y'] = np.full(self.data['Y'].shape, np.nan)
self.data['Sigma_Z'] = []
self.data['Z'] = []
self.data['condition'] = self.modelOptions['kappa']
self.data['t'] = self.modelOptions['ts']
def __init__(self):
AmiciExample.__init__(self)
self.numX = 3
self.numP = 5
self.numK = 4
self.modelOptions['theta'] = np.log10([1, 0.5, 0.4, 2, 0.1])
self.modelOptions['kappa'] = [0.1, 0.4, 0.7, 1]
self.modelOptions['ts'] = np.linspace(0, 100, 50)
self.modelOptions['pscale'] = 2
def __init__(self):
AmiciExample.__init__(self)
self.numX = 6
self.numP = 0
self.numK = 6
self.modelOptions['theta'] = []
self.modelOptions['kappa'] = [0.29, 0.74, 0.44, 0.08, 0.27, 0.18]
self.modelOptions['ts'] = np.linspace(0, 20, 201)
self.modelOptions['pscale'] = 0
self.solverOptions['atol'] = 1e-6
self.solverOptions['rtol'] = 1e-4
self.solverOptions['sens_ind'] = []
self.solverOptions['sensi'] = 0
self.solverOptions['sensi_meth'] = 1
def __init__(self):
AmiciExample.__init__( self )
self.numX = 2
self.numP = 4
self.numK = 0
self.modelOptions['theta'] = np.log10([1, 0.5, 2, 3])
self.modelOptions['ts'] = np.linspace(0, 3, 1001)
self.modelOptions['pscale'] = 2
self.solverOptions['atol'] = 1e-16
self.solverOptions['maxsteps'] = 1e4
self.solverOptions['nmaxevent'] = 10
self.solverOptions['rtol'] = 1e-8
self.solverOptions['sens_ind'] = []
self.solverOptions['sensi'] = 0
self.solverOptions['sensi_meth'] = 1
def __init__(self):
AmiciExample.__init__( self )
self.numX = 3
self.numP = 3
self.numK = 1
self.modelOptions['theta'] = np.log10([0.04, 1e4, 3e7])
self.modelOptions['kappa'] = [0.9]
self.modelOptions['ts'] = np.append(0, 4 * np.logspace(-6, 6))
# logspace output from matlab slightly different:
self.modelOptions['ts'] = [0.0, 4.0E-6, 7.030042499419172E-6, 1.2355374385909914E-5, 2.1714701757295438E-5, 3.8163819053999774E-5, 6.70733174724404E-5, 1.1788206810207238E-4, 2.071789871692485E-4, 3.641192711966091E-4, 6.39943487842423E-4, 0.0011247074791896924, 0.001976685344529533, 0.003474045495005412, 0.006105671868700933, 0.010730783181118898, 0.018859465453829577, 0.03314571091418737, 0.05825393910004978, 0.10238191690798133, 0.17993730675877775, 0.31624172843630804, 0.5557981977492555, 0.97682123781946, 1.7167737040515112, 3.017248025341849, 5.302845462360432, 9.319807242061488, 16.37966024952171, 28.787426920046055, 50.59420867421183, 88.91985930104782, 156.27759748218483, 274.6595380017199, 482.71705625573054, 848.380355168077, 1491.0374881259752, 2620.5142274381983, 4605.5815973057925, 8094.358590900622, 14225.921224892543, 25002.207701095904, 43941.64567950229, 77227.90915533014, 135728.87087581318, 238544.93266378547, 419245.253661875, 736827.9877306866, 1294983.017127056, 2275946.411607322, 4000000.0]
self.modelOptions['pscale'] = 2
self.solverOptions['atol'] = 1e-12
self.solverOptions['rtol'] = 1e-8
self.solverOptions['sens_ind'] = []
self.solverOptions['sensi'] = 0
self.solverOptions['sensi_meth'] = 1
def __init__(self):
AmiciExample.__init__(self)
self.numZ = 1
self.numX = 2
self.numP = 4
self.numK = 2
self.modelOptions['theta'] = np.log10([0.02, 0.3, 65, 0.9])
self.modelOptions['kappa'] = [-60, 10]
self.modelOptions['ts'] = [
0.0, 0.9991997694102649, 1.9983995388205298, 2.9975993082307943,
3.9967990776410596, 4.995998847051324, 5.995198616461589,
6.994398385871854, 7.993598155282119, 8.992797924692384,
9.991997694102649, 10.991197463512913, 11.990397232923177,
12.989597002333443, 13.988796771743708, 14.987996541153972,
15.987196310564238, 16.986396079974504, 17.98559584938477,
18.984795618795033, 19.983995388205297, 20.98319515761556,
21.982394927025826, 22.981594696436094, 23.980794465846355,
24.979994235256623, 25.979194004666887, 26.978393774077155,
27.977593543487416, 28.976793312897684, 29.975993082307944,
30.975192851718212, 31.974392621128477, 32.97359239053874,
33.97279215994901, 34.97199192935927, 35.97119169876954,
36.9703914681798, 37.969591237590066, 38.96879100700033,
39.967990776410595, 40.967190545820856, 41.96639031523112,
42.96559008464139, 43.96478985405165, 44.96398962346192,
45.96318939287219, 46.962389162282456, 47.96158893169271,
48.96078870110298, 49.959988470513245, 50.959188239923506,
51.958388009333774, 52.95758777874404, 53.95678754815431,
54.95598731756456, 55.95518708697483, 56.9543868563851,
57.95358662579537, 58.95278639520563, 59.95198616461589,
60.95118593402616, 61.950385703436424, 62.949585472846685,
63.94878524225695, 64.94798501166721, 65.94718478107748,
66.94638455048775, 67.94558431989802, 68.94478408930829,
69.94398385871854, 70.9431836281288, 71.94238339753907,
72.94158316694934, 73.9407829363596, 74.93998270576986,
75.93918247518013, 76.9383822445904, 77.93758201400065,
78.93678178341092, 79.93598155282119, 80.93518132223146,
81.93438109164171, 82.93358086105198, 83.93278063046225,
84.93198039987251, 85.93118016928278, 86.93037993869305,
87.9295797081033, 88.92877947751359, 89.92797924692384,
90.9271790163341, 91.92637878574438, 92.92557855515463,
93.92477832456491, 94.92397809397517, 95.92317786338542,
96.9223776327957, 97.92157740220595, 98.92077717161622
]
self.modelOptions['pscale'] = 2
#self.modelOptions['qpositivex'] = [0] * self.numX
self.solverOptions['atol'] = 1e-16
self.solverOptions['maxsteps'] = 1e45
self.solverOptions['nmaxevent'] = 22
self.solverOptions['rtol'] = 1e-12
self.solverOptions['sens_ind'] = []
self.solverOptions['sensi'] = 0
self.solverOptions['sensi_meth'] = 1
self.data['Y'] = np.full((len(self.modelOptions['ts']), 1), np.nan)
self.data['Sigma_Y'] = np.full((len(self.modelOptions['ts']), 1),
np.nan)
self.data['Z'] = np.transpose([[
2.4420740245701733, 5.921007525639647, np.nan, 10.366527794075543,
12.83694308382395, 14.624269559247253, 18.722446363647578,
22.20739095602005, 28.602369747827655, 31.442843729542822,
34.01927181474919, 41.26726577405225, 44.275254172160395,
51.56486254598814, 57.100273114298204, 61.961654997481084,
69.03838073191332, 74.3546047146856, 81.21960802401809,
87.2873927650102, 93.34894804384085, 98.57346300859241
]])
self.data['Sigma_Z'] = 0.5 * np.ones(self.data['Z'].shape)
self.data['condition'] = self.modelOptions['kappa']
self.data['t'] = self.modelOptions['ts']
